A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases
Document typeConference report
Rights accessOpen Access
European Commission's projectTRANSACT - Transforming Magnetic Resonance Spectroscopy into a Clinical Tool (EC-FP7-316679)
Machine learning has provided, over the last decades, tools for knowledge extraction in complex medical domains. Most of these tools, though, are ad hoc solutions and lack the systematic approach that would be required to become mainstream in medical practice. In this brief paper, we define a machine learning-based analysis pipeline for helping in a difficult problem in the field of neuro-oncology, namely the discrimination of brain glioblastomas from single brain metastases. This pipeline involves source extraction using k-Meansinitialized Convex Non-negative Matrix Factorization and a collection of classifiers, including Logistic Regression, Linear Discriminant Analysis, AdaBoost, and Random Forests.
CitationMocioiu, V., de Barros, N., Ortega, S., Slotboom, J., Knecht, U., Arús, C., Vellido, A., Julià, M. A machine learning pipeline for supporting differentiation of glioblastomas from single brain metastases. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016". Bruges: I6doc.com, 2016, p. 247-252.
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